Dream-RSI SDK · Alpha

Bring your agent. Record its search. Evolve executable exploration policies.

An independent, unofficial Python SDK inspired by Dream-RSI research. Maintained by TheAstrayDev, who is not a Google or Google DeepMind employee. This is a personal research initiative, not a commercial Google development, official SDK or endorsed product.

Python 3.11+ · zero core third-party dependencies · Apache-2.0 · version 0.2.0a2.

Install

python -m pip install --upgrade dreamrsi==0.2.0a2

The package includes the Python API and the dreamrsi command. Publishing policy packages to GitHub additionally requires GitHub CLI (gh).

Third-party policy and replay packages

Version 0.2.0a2 adds portable JSON bundles and GitHub-hosted package sharing. Community developers can distribute policy versions and recorded discovery trees; recipients import them into local SQLite memory with their own agent, quality metric, task-family contract and acceptance rules. These replay datasets contain observed search transitions, not model weights. Saved policies can avoid repeated development when they meet the recipient's quality floor; new tasks still run the recipient's agent.

dreamrsi list
dreamrsi install OWNER/REPOSITORY
dreamrsi save my-policy-pack --all
dreamrsi publish .dreamrsi/packages/exports/my-policy-pack.dreamrsi.json

Use a real package reference for OWNER/REPOSITORY. Export requires previously saved memory; publication requires GitHub CLI and creates a public repository. See the complete walkthrough for the diagram, executable integration example, and compatibility requirements.

Basic agent integration

from dreamrsi import Budget, DreamRSI

rsi = DreamRSI(
    agent=lambda task: task.upper(),
    evaluator=lambda answer: float(len(answer)),
    budget=Budget(model_calls=4),
)
result = rsi.run_sync("hello")
print(result.best)  # HELLO

This example demonstrates integration, not quality improvement. Stateful adapters support generate/evaluate/refine tasks. Replay uses recorded outcomes without new discovery calls. LLMPolicyDeveloper writes and iteratively rewrites executable source from measured feedback. Version 0.2.0a1 adds opt-in persistent champion reuse and more conservative replay cost accounting. It can replay already recorded runs without a new training call, delays holdout collection until a replay-improving candidate exists, and stops repeated policy revisions. The default promotion gate requires paired raw-quality and probe evidence; applications needing score-only decisions can explicitly use ReplayOnlyGate. An end-to-end cost advantage for an LLM discovery agent has not been proven. The SDK includes configurable Docker-free policy interpreters, optional process execution, held-out validation, SQLite recovery and reported token/USD accounting.

Two measured experiments

In the Bonsai Q2 experiment, a real local model wrote executable policy code while a deterministic agent solved the tasks. Across 64 fresh fixture tasks, counted operations fell from 256 to 248 with raw quality 0.9 throughout. This is a logical-operation proxy, not a measured token or dollar saving for an LLM agent.

In the GPT-6 Luna xhigh experiment, the real model both solved tasks and developed policies. Deployment fell from 24 to six model requests across six held-out tasks, but 94 preparation requests made the full path 100 versus 24. Mean reported score was slightly lower. This demonstrates learning and reuse, not an all-in economic win.

• Full documentation and roadmap
• Bonsai Q2 experiment
• GPT-6 Luna experiment
• Integration and recovery
• Sandbox configuration
• Issues
• Original Dream-RSI paper

Alpha APIs may change. Generated source runs in a bounded Python-syntax subset, not arbitrary Python. External state isolation and remote cancellation require adapter support. Usage caps depend on accurate provider reports and ceilings. Research-scale performance and broad generalization remain unverified.